Machine learning methods usually learn a model that uses training data to map feature vectors to known classes [1]}, [2]}.
Testing these methods consists in dealing with unknown samples of known classes.
However, dealing with unknown classes on testing is not trivial.
Open-set face recognition methods have to deal with an “unlimited” number of unknown classes (subjects' identities) when compared to the countable quantity of individual identities enrolled in the gallery set [3]}.
